System and method for physiological health simulation

ABSTRACT

Systems and methods for health and body simulations in order to predict numerous physiological parameters in a subject or a population of subjects based on the input of limited physiological data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. patent applicationSer. No. 15/096,022, filed Apr. 11, 2016, now U.S. Pat. No. 10,398,389,the disclosure of which is herein incorporated by reference in itsentirety.

FIELD OF THE INVENTION

This invention relates to methods and systems for modeling andpredicting the progression of human disease including, for example,diabetes.

BACKGROUND

We live in a new era of data availability, understanding of humanphysiology, and computing power with lives being remotely monitored dueto sensor availability and the expansion of electronic medical records(EMRs). We can use these new advances to change how we approach healthusing simulation modeling much like weather modeling in 1960s andnuclear weapons simulations in 1980s. If we can use science andphysiology to understand the impact of health interventions prior toimplementation, we can calculate the economic value of an intervention,to build a business case prior to an implementation, and identify theright behaviors for change.

Currently, systems used to study human physiology model singularphysiological processes in the body without taking into account theimpact of rich interconnections and feedbacks between the many processesin the whole body. These feedbacks and interconnections betweenphysiological processes combined with our improved understanding of thesystems are critical for understanding the observed health state of anindividual. Interventions modeled without considering whole bodyphysiology are therefore insufficient to simulate health interventionsat the level of an individual and cannot connect overall health outcomesto interventions.

In view of the foregoing, a need exists for an improved system andmethod for physiological health simulation in an effort to overcome theaforementioned obstacles and deficiencies of conventional systems.

SUMMARY OF THE INVENTION

One aspect includes a method for predicting a time to diabetes onset ina subject that includes (a) determining for the subject a subjectparameter set values comprising height, weight, age, gender, and serumHbA1c concentration at a first time; (b) calculating initial parameterset values, comprising basal metabolic rate, excess caloric intake,EC50(FFA), and beta cell apoptosis rate, from the subject parameter setby (i) setting an age estimate to an age younger than the age determinedin step (a), setting a weight estimate to a weight that is differentfrom the weight determined in step (a), and setting a serum HbA1cestimate to a value different from the serum HbA1c concentrationdetermined in step (a), (ii) providing an initial estimate for eachvalue in the initial parameter set values at the age estimate, (iii)iterative calculating initial parameter set values, weight estimate, andserum HbA1c estimate, in a time-dependent manner in which the ageestimate is increased by a first time step in each iteration from theage estimate set is step (b)(i) to the age, such that the weightestimate is substantially equal to the weight and the serum HbA1cestimate is substantially equal to the serum HbA1c concentration whenthe age estimate is substantially equal to the age, (c) iterativelycalculating at a future time, in a time-dependent manner in which thetime is increased by a second time step, a projected serum HbA1cconcentration using the initial parameter set calculated in step(b)(iii) and the subject parameter set values, until the projected serumHbA1c concentration at the second time is calculated to be greater thanor equal to 6.5%, (d) identifying the future time iteratively calculatedin step (c) as the time to diabetes onset, and (e) displaying the futuretime identified in step (d) as the predicted time for diabetes onset.

Another aspect includes a method for predicting a time to diabetes onsetin a subject, the method comprising (a) determining for the subject asubject parameter set values comprising height, weight, age, gender,fasting blood glucose concentration, and serum HbA1c concentration at afirst time; (b) calculating initial parameter set values, comprisingbasal metabolic rate, excess caloric intake, EC50(FFA), and beta cellapoptosis rate, from the subject parameter set by (i) setting an ageestimate to an age younger than the age determined in step (a), settinga weight estimate to a weight that is different from the weightdetermined in step (a), setting a fasting blood glucose concentrationestimate to a value different from the fasting blood glucoseconcentration determined in step (a), and setting a serum HbA1c estimateto a value different from the serum HbA1c concentration determined instep (a) (ii) providing an initial estimate for each value in theinitial parameter set values at the age estimate, (iii) iterativecalculating initial parameter set values, weight estimate, fasting bloodglucose concentration estimate, and serum HbA1c estimate, in atime-dependent manner in which the age estimate is increased by a firsttime step in each iteration from the age estimate set is step (b)(i) tothe age, such that the weight estimate is substantially equal to theweight, the fasting blood glucose concentration estimate issubstantially equal to the fasting blood glucose concentration, and theserum HbA1c estimate is substantially equal to the serum HbA1cconcentration when the age estimate is substantially equal to the age,(c) iteratively calculating at a future time, in a time-dependent mannerin which the time is increased by a second time step, a projectedfasting blood glucose concentration using the initial parameter setcalculated in step (b)(iii) and the subject parameter set values, untilthe projected fasting blood glucose concentration at the second time iscalculated to be greater than 125 mg/dL, (d) identifying the future timeiteratively calculated in step (c) as the time to diabetes onset, and(e) displaying the future time identified in step (d) as the predictedtime for diabetes onset.

In some embodiments, the fasting blood glucose concentration estimateset in step (b)(i) is less than 100 mg/dL.

Another aspect includes a method for predicting a future blood glucoseconcentration in a subject, the method comprising: (a) determining forthe subject a subject parameter set values comprising height, weight,age, gender, fasting blood glucose concentration, and serum HbA1cconcentration at a first time; (b) calculating initial parameter setvalues, comprising basal metabolic rate, excess caloric intake,EC50(FFA), and beta cell apoptosis rate, from the subject parameter setby (i) setting an age estimate to an age younger than the age determinedin step (a), setting a weight estimate to a weight that is differentfrom the weight determined in step (a), setting a fasting blood glucoseconcentration estimate to a value different from the fasting bloodglucose concentration determined in step (a), and setting a serum HbA1cestimate to a value different from the serum HbA1c concentrationdetermined in step (a) (ii) providing an initial estimate for each valuein the initial parameter set values at the age estimate, (iii) iterativecalculating initial parameter set values, weight estimate, fasting bloodglucose concentration estimate, and serum HbA1c estimate, in atime-dependent manner in which the age estimate is increased by a firsttime step in each iteration from the age estimate set is step (b)(i) tothe age, such that the weight estimate is substantially equal to theweight, the fasting blood glucose concentration estimate issubstantially equal to the fasting blood glucose concentration, and theserum HbA1c estimate is substantially equal to the serum HbA1cconcentration when the age estimate is substantially equal to the age,(c) iteratively calculating at a future time, in a time-dependent mannerin which the time is increased by a second time step, a projectedfasting blood glucose concentration using the initial parameter setcalculated in step (b)(iii) and the subject parameter set values, and(d) displaying the projected fasting blood glucose concentrationiteratively calculated in step (c) at a plurality of future times.

In some embodiments, the fasting blood glucose concentration estimateset in step (b)(i) is less than 100 mg/dL.

Another aspect includes a method for predicting a future weight of asubject, the method comprising: (a) determining for the subject asubject parameter set values comprising height, weight, age, gender, andserum HbA1c concentration at a first time; (b) calculating initialparameter set values, comprising basal metabolic rate, excess caloricintake, EC50(FFA), and beta cell apoptosis rate, from the subjectparameter set by (i) setting an age estimate to an age younger than theage determined in step (a), setting a weight estimate to a weight thatis different from the weight determined in step (a), and setting a serumHbA1c estimate to a value different from the serum HbA1c concentrationdetermined in step (a), (ii) providing an initial estimate for eachvalue in the initial parameter set values at the age estimate, (iii)iterative calculating initial parameter set values, weight estimate, andserum HbA1c estimate, in a time-dependent manner in which the ageestimate is increased by a first time step in each iteration from theage estimate set is step (b)(i) to the age, such that the weightestimate is substantially equal to the weight, and the serum HbA1cestimate is substantially equal to the serum HbA1c concentration whenthe age estimate is substantially equal to the age, (c) iterativelycalculating at a future time, in a time-dependent manner in which thetime is increased by a second time step, a projected weight using theinitial parameter set calculated in step (b)(iii) and the subjectparameter set values, and (d) displaying the projected weightiteratively calculated in step (c) at a plurality of future times.

Another aspect includes a method for predicting a future mass ofpancreatic beta cells in a subject, the method comprising: (a)determining for the subject a subject parameter set values comprisingheight, weight, age, gender, serum insulin concentration, serum glucoseconcentration, and serum HbA1c concentration at a first time; (b)calculating initial parameter set values, comprising basal metabolicrate, excess caloric intake, EC50(FFA), and beta cell apoptosis rate,from the subject parameter set by (i) setting an age estimate to an ageyounger than the age determined in step (a), setting a weight estimateto a weight that is different from the weight determined in step (a),setting a serum insulin concentration estimate to a serum insulinconcentration that is different from the serum insulin concentrationdetermined in step (a), setting a serum glucose concentration estimateto a serum glucose concentration that is different from the serumglucose concentration determined in step (a), and setting a serum HbA1cestimate to a value different from the serum HbA1c concentrationdetermined in step (a), (ii) providing an initial estimate for eachvalue in the initial parameter set values at the age estimate, (iii)iterative calculating initial parameter set values, weight estimate,serum insulin concentration estimate, serum glucose concentrationestimate, and serum HbA1c estimate, in a time-dependent manner in whichthe age estimate is increased by a first time step in each iterationfrom the age estimate set is step (b)(i) to the age, such that theweight estimate is substantially equal to the weight, the serum insulinconcentration estimate is substantially equal to the serum insulinconcentration, the serum glucose concentration estimate is substantiallyequal to the serum glucose concentration, and the serum HbA1c estimateis substantially equal to the serum HbA1c concentration when the ageestimate is substantially equal to the age, (c) iteratively calculatingat a future time, in a time-dependent manner in which the time isincreased by a second time step, a projected mass of pancreatic betacells using the initial parameter set calculated in step (b)(iii) andthe subject parameter set values, and (d) displaying the projected massof pancreatic beta cells iteratively calculated in step (c) at aplurality of future times.

In any of the foregoing aspects of the invention, the age estimate setin step (b)(i) is at least one year less than the age.

In any of the foregoing aspects of the invention, the weight estimateset in step (b)(i) is less than the weight.

In any of the foregoing aspects of the invention, the serum HbA1cestimate set in step (b)(i) is less than 3%.

In any of the foregoing aspects of the invention, the first time step is5-365 days.

In any of the foregoing aspects of the invention, the first time step isconstant for all iterations.

In any of the foregoing aspects of the invention, the second time stepis 5-365 days.

In any of the foregoing aspects of the invention, the second time stepis constant for all iterations.

By “excess caloric intake” is meant the caloric intake of a subjectwhich exceeds the amount of calories consumed over the same time periodas calculated using the actual or estimated basal metabolic rate (BMR).For convenience, the BMR and excess caloric intake is expressed askcal/day but these measures may be expressed as any suitable measure ofnutritional energy equivalent (e.g., kilojoules) and/or over anysuitable time period (e.g., hours, days, weeks, years, etc.).

By “EC50(FFA)” is meant the half maximal free fatty acid (FFA)concentration in muscle that induces insulin resistance in a subject.

By “beta cell apoptosis rate” is meant the proportion of existingpancreatic beta cells in a subject that undergo apoptosis, as a functionof time. For convenience, this is expressed as a proportion (i.e., rangeof 0-1) and is expressed on a daily basis.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an exemplary flow chart illustrating an embodiment of a methodof simulating a biological process.

FIG. 2 is an exemplary flow chart illustrating an embodiment of a methodfor setting up and running a simulation model for a health scenario inaccordance with the method of FIG. 1 .

FIG. 3 is an exemplary flow chart illustrating an embodiment of themethod for calibrating a model simulation in accordance with the methodof FIG. 2 .

FIG. 4 is an exemplary flow chart illustrating an embodiment of a methodof testing and selecting body intervention programs.

FIG. 5 is an exemplary top-down diagram illustrating an embodiment ofcomputational modules which support the biological processes implicatedfor the development of pre-diabetes and onset of diabetes.

FIG. 6 illustrates one exemplary embodiment of the blood module of FIG.5 .

FIG. 7 is an exemplary diagram illustrating one embodiment of the musclesubcomponent of the metabolism module of FIG. 5 .

FIG. 8 is an exemplary diagram illustrating one embodiment of the liversubcomponent of the metabolism module of FIG. 5 .

FIG. 9 is an exemplary diagram illustrating one embodiment of theadipose tissue subcomponent of the metabolism module of FIG. 5 .

FIGS. 10A-B are exemplary data flow diagrams of another portion of oneembodiment of the insulin module of FIG. 5 .

FIG. 11 is an exemplary data flow diagram of another portion of oneembodiment of the insulin module of FIG. 5 .

FIGS. 12A-D and 13A-C show bar graphs of the body weight for womeninitially between the ages of 50-55 starting in 1999 and then in 2001,2003, 2005, 2007, 2009 and 2011 respectively.

FIG. 14 is a graph of a set of calibration data over time based on thedata illustrated in FIGS. 12 and 13 .

FIGS. 15A-D illustrate four sample subjects to demonstrate the method ofsimulation disclosed in FIGS. 1-4 .

FIGS. 16A-D illustrate the comparison of experimental data and modeloutput representing a distribution of weight and HbA1C as a baseline forthe placebo population (A-B) and predictions for thelifestyle-modification arm (C-D).

FIG. 17 illustrates one example of fitting to a first data set andpredicting a second data set (data for weight, fat mass and fat freemass) for 2 example individuals.

FIG. 18 illustrates one embodiment of an exemplary computer architecturefor use with the present method of simulating a biological process ofFIG. 1 .

It should be noted that the figures are not drawn to scale and thatelements of similar structures or functions are generally represented bylike reference numerals for illustrative purposes throughout thefigures. It also should be noted that the figures are only intended tofacilitate the description of the preferred embodiments. The figures donot illustrate every aspect of the described embodiments and do notlimit the scope of the present disclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Currently available models of human physiological systems are deficientbecause they model body systems in isolation without accounting for therich connections between systems. Thus, there is a need for an improvedphysiological health and body simulation system that can provide aplatform for a wide range of applications, such as selection of costeffective health interventions, guiding users through a healthintervention, and understanding the impact of lifestyle, drugs, ormedical devices. This result can be achieved, according to the healthand body simulation systems disclosed herein. The health and bodysimulations may be provided as a stand-alone module or may be integratedinto other health and wellness products including, for example, dietaryand fitness tracking programs and applications for research anddevelopment in drug development.

In various embodiments, and as described in more detail herein, thehealth and body simulation system can be configured to model humanphysiology to predict future health states, effects of various healthintervention strategies and optimize these interventions at anindividual patient level in a medical setting with the end goal ofenabling health providers to choose patient-specific combinations ofpharmaceutical and/or lifestyle interventions; and/or keeping patientsengaged by making them aware of long term consequences of lifestylechoices and enabling them to make informed decisions. Additionally, insome embodiments, the health and body simulation system can beconfigured to integrate patient-specific data from multiple sources intoa single platform that can consume this data and provide useful insightsand predictions about body health on an individual, group or demographiclevel.

The patient data can come from a variety of sources. For example,patient specific electronic medical records (EMRs) can be available tothe provider, and these records can contain physiological measurementssuch as blood pressure, blood glucose, previous medication history, andthe like. Patient specific data can also be available from wearable userdevices such as fitness bands, pedometers, smart watches, and the like.Some user devices, and the health and body simulation system generally,also may allow for the manual entry of data including, for example,lifestyle or user information such as type, intensity and duration ofexercise, sleep patterns, gender, height, weight, and the like.

In various embodiments, the present system can be configured to allowpayers, providers, pharmaceutical and life science companies, employers,government, and wellness organizations, and the like, to leverage thepredictive power of the simulation module to identify future high-riskpatients. Identification of future high-risk patients can allow forearly interventions to prevent future over-utilization of healthresources. Additionally, in various embodiments, organizations canquantify effects of intervention programs on populations as described inmore detail herein. By providing a system dynamics approach to modeling,the body simulation module can provide mechanism-based quantitativeanalysis to drive and inform complex business decisions on health andwellness expenditures.

In further embodiments, as described herein, the body simulation modulecan be used to simulate clinical trials, which are expensive with longlead-times and associated with high risks of failure. In variousembodiments, the body simulation module can provide for an initialassessment of new pharmaceutical products in a virtual, non-invasive insilico environment. Pharmaceutical and life science companies, academicinstitutions and the like can thereby evaluate product efficacy on alarge simulated population prior to a full clinical trial allowing theorganizations to manage their pipeline and investment dollars moreeffectively. Such a strategy of using simulation results to supplementexperimental and clinical evidence is currently promoted by the Food andDrug Administration (FDA). Additionally, companies can use simulatedclinical trials to identify adverse side effects arising from complexsystemic interactions. In an environment of increasing regulation ofpharmaceutical products, this extra layer of protection can identify andmitigate potential risks early in the drug development process.

In some embodiments, consumer-facing organizations can use the bodysimulation module to design and develop applications to provide personalbehavior nudges and guide individuals to better levels of personalizedhealth. The body simulation module therefore can provide quantitative,scientific backing for external applications to recommend behavioralchanges and inform individuals to make educated health decisions.

In further embodiments, the body simulation module can be configured toallow clinicians to proactively model the relationships between bodysubcomponents to understand treatment effects in multiple sub-systems ofthe body. Providers can use the body simulation module to simulate avariety of intervention options to understand the potential impact ofeach, which can then inform their clinical decisions. By runningoptimization simulations for an individual, the right specifications ofinterventions can be identified.

Implementation of the Health and Body Simulation

In various embodiments, the health and body simulation can be used tomodel various health scenarios, which can include specific biologicalfunctions such as metabolism, inflammation, respiration, or the like.Additionally, health scenarios can relate to how physiologicalconditions such as a low carbohydrate diet, high exercise regimen, orthe like, will globally impact the physiology of a subject.

FIG. 1 illustrates a method 1000 of simulating a biological process inaccordance with an embodiment of the invention. The method 1000 begins,in block 1010, where a health scenario is selected for modeling, and, inblock 1020, raw health data is obtained. Raw health data can includepublished clinical studies, claims data, prescription data, patientbiomarkers, patient personal characteristics and lifestyle, consumerdata, and the like. Additionally, sources of raw health data can includeclinical data, user wearables, and the like.

In block 1030, the raw health data is cleaned and processed. Forexample, data cleansing can be desirable to ensure that the raw data isnot inconsistent and is structured in a standardized format that can beinput into the simulation module. In some embodiments, raw health datacan be cleaned by a script that removes incomplete/incorrect data inorder to produce a clean data set.

In block 1040, a determination is made whether the health data issufficient to model the selected health scenario. For example, data canbe considered complete if all the information to be supplied to themodel is present in, or can be obtained from the provided health data.If the health data is not sufficient, then the method 1000 continues, toblock 1050, where filler health data is obtained (or an attempt is madeto obtain such filler health data). For example, when modeling aspecific patient, health data about that patient may be provided, butsuch data may not be sufficient to properly setup the body model asdescribed herein. In such cases, using information about the user,demographic or other such population or study data can be used to fillin missing data with data that is intended to represent the patient.

Similarly, in embodiments where a specific population of individuals isbeing modeled and data from this specific population is provided,filler-data can be data from a similar population that is intended torepresent the specific population that is being modeled. In someembodiments, proprietary data can be obtained from private sources, suchas commercial companies, hospitals, insurers, or the like. Public datasets can come from online sites, public feeds, published studies,government census data, and the like. Portions of these datasets can beused to fill in any missing biomarkers or characteristics needed toprovide inputs to setup a given modeling task or scenario.

In block 1060, a determination is made whether the current set of healthdata (including filler data) is sufficient to model the health scenario,and, if not, an error is indicated in block 1070. However, if the healthdata is sufficient to run the health scenario, the method 1000continues, to block 1030, where the data is again cleaned and processesif necessary.

Returning to block 1040, if the set of health data is sufficient to runthe selected health scenario, the method 1000 continues, to block 2000,where a simulation model for the selected health scenario is run asdescribed herein and shown in FIG. 2 . In block 1090, the simulationresults are presented. For example, results can include suitable resultsdata including body weight, insulin resistance, time to diabetes onset,progression of diabetes past onset, responsiveness to drugs and/orlifestyle changes (e.g., diet and exercise), time course of pancreaticdysfunction, time to need for pancreatic transplant, and the like.

FIG. 2 illustrates an exemplary method 2000 for setting up and running asimulation model for a health scenario. The method 2000 begins in block2010 where variable T is set to 0, and, in block 2020, the modelstructure is setup. In various embodiments, the model structure can be acombination of biological functions that is necessary to generate thedesired results for a health scenario. The model structure can bedetermined based on the health scenario that is initially selected(e.g., in block 1010 of FIG. 1 ). Setting up a model structure cancomprise selecting one or more modules and/or one or more connectionsbetween modules. In some embodiments, such modules and connections canbe individually selected and configured. In other embodiments, selectedhealth scenarios can be associated with a default set of selectedmodules and/or connections, which may or may not be modified if desired.

The method 2000 continues to block 2100, where model parameters areidentified and calibrated as described in more detail herein. In block2040, the simulation model is run at the current time step T withcurrent health parameters and the current set of health data. In block2050, a determination is made whether the simulation is complete, and,if not, the method 2000 continues, to block 2060, where variable T isincremented, and the method 2000 cycles back to block 2040. For example,as discussed herein, a health scenario can be run over a selected timeinterval with selected time steps.

In various embodiments, time interval and/or time steps can be inseconds, minutes, hours, days, weeks, months, years, decades, or thelike. For example, the time interval can be forty years with a time stepof three months. In various embodiments, desired time intervals can beeither based on clinical guidelines, extrinsic economic factors, or thelike.

When running a health scenario, changes in each module can affect othermodules as data is passed to/from the modules. Accordingly, as thevariables of a given module change and affect other modules or themodule is affected by other modules, the processes of a body can besimulated.

FIG. 3 illustrates the exemplary method 2100 (shown in FIG. 2 ) forcalibrating a model simulation in further detail. The method 2100begins, in block 2110, where an initial model simulation parameter setis generated. In various embodiments, parameters are numericalquantities (e.g., time-independent constants), which are used inmathematical equations that describe relationships of system variablesto inputs or relationships between system variables. For example, therate at which glucose is converted to glycogen in muscle can beproportional to the concentration of glucose in muscle. Theproportionality constant, say k, relating the rate of conversion toglucose concentration is an example of a parameter. Each module and thecomponents and connections thereof can include one or more parameter. Insome embodiments, numerical values of some of these parameters may beexactly known, while others may be unknown.

However, even if parameters such as k are not uniquely known, numericalranges over which such parameters could vary can be determined. Forexample, while the value of k may not be precisely known, an initialestimate could suggest that k lies between 10 and 50 seconds⁻¹. Thus(10, 50) seconds' can be an initial estimate of the range of parameterk.

In some embodiments, an initial estimate of such a range can be obtainedfrom scientific literature, or other suitable source, in various ways.For example, previously published models of similar systems may suggestlikely ranges of parameters, or the like. Accordingly, data observed inexperimental studies related to a given process can be examined andinitial ranges can be estimated based on the results of such studies. Inthe absence of any information from the literature or other sources,ranges can be empirically determined by iterative trials, a random guesscan be made, or a default value can be used.

In various embodiments, a parameter estimation process can be utilizedto estimate unknown parameters. For example, an initial guess can begenerated by randomly choosing a number lying within a range determinedfor a parameter as described above. Continuing the example above,parameter k, whose range is estimated to be (10, 50) seconds⁻¹, aninitial guess could be 35.47 seconds⁻¹. In some embodiments, a computeralgorithm can pick this random value in the specified range in anunbiased manner. Similarly, a set of initial guesses for other unknownparameters can be generated until all parameters of the model have beenassigned numerical values, either as an initial guess, as a parameterwith known value, or the like.

In block 2120, a model simulation is generated with the currentparameter set. For example, once the model is uniquely defined by thevalues assigned to parameters, the model can be simulated by solving theset of differential, algebraic equations, or the like, that defines themodel as embodied in the modules 230 and interconnections of the modules230 that describe the biological system being modeled.

In various embodiments, a simulation result can be a matrix of numbersdescribing time-dependent changes in the values of all the variablescomprising the model. The entire matrix, or parts of it, can bepresented visually or in any other suitable form such as a table, linecurve, bar graph, or the like. For example, after simulating the modelwith k=35.47 seconds⁻¹, a matrix of numbers describing the dynamics ofall variables in the model could be obtained. From this matrix, it canbe possible, for instance, to select the rows or columns containinginformation about the dynamics of concentration of blood glucose overtime (e.g., denoted by the variable G(t)).

In block 2130, output from the model simulation is evaluated via atraining data set, and, in block 2140, a determination is made whetherthe simulation output is able to replicate the values of thecorresponding variables in the training data set within a desired errortolerance. In various embodiments, training data can comprise a data setobtained from scientific literature, publicly available databases, asource that contains experimentally measured data about the variablespresent in the model, or the like. Accordingly, the purpose of atraining data set can be to provide data that can be used to estimatethe parameters of the model.

For example, among other variables, a model can simulate theconcentration of blood glucose (represented by G(t) as discussed above).Assuming the model is being trained to represent a healthy individual,an example of a training data set could be experimentally measuredconcentrations of blood glucose in several healthy individuals over aperiod of time.

The variables of a given model can correspond to variables available inthe training data, which can be isolated. For example, time dependentblood glucose concentration, G(t), can be extracted from the completemodel output as described above. The numerical values of blood glucoseas predicted by the model can then be compared with the numerical valuesof blood glucose available in the training data. A numerical score orfitness score can be assigned to the model depending on how well themodel simulated values match the training data. For example, in someembodiments, the fitness score can be designed to be low for a goodmatch and high for a poor match between model output and training data.

To determine whether the fitness score is acceptable, the fitness scorecan be compared to a pre-determined threshold value. For example, if thefitness score is below this threshold, the model can be deemed todescribe the training data set well, and the method 2100 continues, toblock 2180, where the current parameter set is accepted. In variousembodiments, the tolerance limit can be a number whose value is chosenon a case-by-case basis using expert judgment.

For example, in a particular calibration run the tolerance limit couldbe ten. If the fitness score is less than or equal to ten, the parametervalues are accepted as a good estimate. If the fitness score is greaterthan ten, the parameters are refined further, and the method 2100continues to block 2150, where a determination is made whetherimprovement iteration has timed out. For example, if the parameters arechanged and refined over a large number of iterations but do notconverge on values that create an output within a desired errortolerance, the method 2150 can present an error indication, in block2170. In such a case, a user can have an opportunity to modify theparameters, change the error tolerance, change the input data, changethe model connections or module configuration, or make other changesthat might generate a suitable simulation that would produce an outputthat is within a desired error tolerance.

However, returning to block 2150, if the improvement iteration has nottimed out, the method 2100 continues, to block 2160, where the currentparameter set is modified. Accordingly, in various embodiments, theprocess of calibration can be an iterative one, and a new set ofcandidate parameter values can be generated in each iteration. Suchcandidate values can result in an increased, decreased, or unchangedfitness scores, thus making the simulation less, more or equallyaccurate over the iterations. In various embodiments, the training dataremains the same over each iteration/simulation. In some embodiments,successive iterations are compared based on their fitness scores, andthe change in fitness score can be used to determine how to modify theparameters in a successive iteration.

In various embodiments, a perturbation factor can be added to thevariables in some iterations. For example, in some embodiments, if asubsequent iteration has reduced fitness, un-changed fitness orincreased fitness, a perturbation factor can be added. Such aperturbation factor can change over iterations or remain the same. Forexample, if adding the perturbation makes the simulation output lessaccurate or unchanged, the perturbation factor can be adjusted. In someembodiments, direction and/or magnitude of the perturbation factor canbe determined via an algorithm or by a human operator.

Testing and Selection of Intervention Programs Using the Health and BodySimulation

In various embodiments, it can be desirable to test and identify healthinterventions that can improve the health of a given patient orpopulation. For example, population health prediction continues to beimportant for employers, insurance companies, wellness organizers, orany organization holding risk in the health ecosystem. Healthorganizations that hold risk continue to introduce new and innovativemethods to nudge individuals towards a healthier state. Accordingly, invarious embodiments, a body simulation model can be used to quantify thefuture health state of an individual or population based on currentavailable data, and selecting a suitable health intervention can bebased on a desired health outcome. For example, a desired outcome caninclude reduction in fasting glucose, improved results from a glucosetolerance test, weight loss, chronic disease (e.g., diabetes)prevention, and the like.

FIG. 4 illustrates an exemplary method 4000 of testing and selectingbody intervention programs. The method 4000 begins, in block 4010, wherea set of intervention programs is selected. For example, based on thedesired outcome, a list of types of intervention programs can begenerated that could potentially achieve the desired outcome. In oneexample, interventions related to diabetes prevention may include a dietcontrol only intervention, activity change only intervention, or a dietand activity change intervention. In various embodiments, interventionprograms can include increased and/or decreased cardiovascular training,resistance training, sleep, macronutrient specific diets, or the like todeliver the optimal outcomes with minimal interventions. In someembodiments, an intervention program can include changes in diet, druguse, smoking, stress, or the like.

For example, different intervention programs can include differentconsumption rates of different macronutrients such as carbohydrates,fat, cholesterol, and the like. In various embodiments, alternativeintervention programs can include several values for a given change(e.g. 5%, 10%, 15% change in carbohydrate intake). Additionally, someintervention programs may have changes in a plurality of such health orbody related variables or changes.

In intervention looping block 4020, a loop begins for each of theselected body intervention programs, and, for each selected bodyintervention program, the method 4000 continues to block 4030, wherebody physiology changes corresponding to the intervention program areidentified by simulating the intervention. In various embodiments, agiven intervention program can change input data for a given modeland/or can change the parameters of the model. For example, for dietrelated interventions such parameters can include modifying the intakeof protein, carbohydrate, fat, fiber, or the like. Additionally, smallerscale nutritional information such as specific types of carbohydrates,fats, and protein can also be used in some embodiments. Additionally, infurther embodiments, nutritional information from consumer retailproducts can be used.

In block 2000, a simulation model is run for the intervention programand, in block 4050, an effectiveness of the intervention program isdetermined. For example, effectiveness can be determined based on achange in a specific clinical marker of interest, including A1c levels,or risk of diabetes, body weight, or the like. If the desired healthoutcome was prevention of onset of diabetes over the next ten years,then a model output for a specific intervention can be whether there isonset of diabetes over the next ten years or a percent chance that onsetof diabetes will occur.

Health-effectiveness can be determined by the degree of change in themodel output of interest (e.g., serum HbA1c level) due to changesintroduced by the intervention. For example, while multipleinterventions could prevent onset of diabetes, one intervention couldproduce a larger difference than the other (e.g. one intervention bringsthe final serum HbA1c level to just below 6.5 while another brings itcloser to 5.7 (pre-diabetes onset)). Examples of not being effectivewould be an insignificant or insufficient reduction the serum HbA1clevels.

In block 4060, the cost of the intervention program is determined. Forexample, the cost of a given intervention program can be estimated inone of several possible ways, including calculating cost of a medicationover a time period, cost of health care services over a time period,cost of a gym membership of the time period, and the like. In variousembodiments, program cost can be computed through research of comparableintervention plans.

In block 4070, the loop for all selected body intervention programsends. In other words, once all intervention programs have beenevaluated, the method 4000 continues, to block 4080, where the set ofbody intervention programs is ranked. Such ranking can be done in one ofmany suitable ways. For example, in various embodiments, cost andeffectiveness of a given program can be assigned different weightingfactors and a rank could be generated by combining the weighted cost andweighted effectiveness. In some embodiments, cost may not affect anintervention unless cost reaches a given threshold (e.g., an insurancelimit). In other embodiments, cost and effectiveness can receive similarweighting in generating a rank because finding the lowest cost, but mosteffective solution may be desirable. In further embodiments, acost/benefit analysis can include consideration of cost to implementintervention and cost associated with health at end of intervention andprojected future health state of the population or patient.

In block 4090, one or more of the body intervention programs areselected based on rank. For example, in some embodiments, the top-rankedprogram can be selected. In other embodiments, a plurality of top-rankedprograms (e.g., the top three) can be selected.

Diabetes Model

Simulation modeling of diabetes and related physiological parameters isdescribed in detail here to illustrate the principles of the inventionoutlined above. It is understood that these principles may be applied toany simulation described above.

Diabetes was elected as the simulation to exemplify the principles ofthis invention because it is a huge burden on the US healthcare economycosting $174 billion per year (estimates from 2007). The latest reportsfrom the CDC project that the prevalence of diabetes is expected to growfrom current estimates of around 14% to 25-28% in 2050. A closer lookshows that the two primary reasons for this projected increase indiabetes prevalence are: (a) the growth of minority communities whereprevalence is particularly high due to factors including geneticpredispositions and lifestyle choices and (b) increased life expectancydue to the advancement of medical care and new drugs, allowing diabeticpatients to live longer than before. It is well established inliterature that diabetes significantly increases the risk for severalco-morbidities that include cardiovascular disease, nephropathy,neuropathy, risk of amputation etc. Hence, diabetes is not just ahealthcare problem; it is an economic problem.

Studies on larger populations of diagnosed diabetic patients found that57.5% of the subjects had uncontrolled or unmanaged HbA1c (>9%). Thereis a need for improved care for diagnosed diabetes through a combinationof pharmacological and lifestyle interventions such that HbA1c levelsare better controlled. However, when it comes to diabetic patients thereseems to be confusion regarding the goal for a lifestyle intervention.In the pre-diabetic population, it has been shown that a body-weightreduction of greater than 6% leads to significant reduction in risk foronset of diabetes. However, it remains unclear whether the goal forlifestyle interventions should be to achieve weight loss, increasedphysical activity, or improved HbA1c target.

Previous mathematical models of human metabolism have focused on energybalance in the body. The manifestation described here primarily focuseson mass balance of three major macronutrients in the body—carbohydrates,protein and fat. It is assumed that the changes in energy consumption byprocesses other than aerobic respiration (e.g., gluconeogenesis) aresmall and can be ignored compared to changes in energy requirements forregular body functions and physical activity. Within the scope of thisassumption, the model also balances the energy of the entire system.Decreases in energy expenditure (e.g., due to reduced physicalactivity), keeping the intake of macronutrient the same, lead toincreases in weight predominantly through an increase in fat mass.

The present disclosure is exemplified using a computational model thatbrings together all relevant systems from macronutrient mass balance,insulin secretion, and inflammation to develop a more comprehensiverepresentation of the systems. The model supports the biologicalprocesses which have been implicated for the development of pre-diabetesand onset of diabetes.

The computational model of diabetes is a top-down designed modelcomposed of 6 compartments that together reproduce the major biologicalprocesses which lead to the development of insulin resistance,development of pre-diabetes, and onset of diabetes in patients (FIG. 5). The model spans 4 scales—from molecular, cellular, tissue/organ towhole-body. The model is divided into (1) a blood module, (2) ametabolism module that has submodules of (i) muscle, (ii) liver, and(iii) adipose tissue, (3) an insulin sensitivity/resistance module, and(4) an insulin production module. These modules and submodules arerepresented in FIG. 5 . The scientific reasoning behind the choice ofthese specific compartments is driven by the ability of the minimalmodel to reproduce macronutrient metabolism as it pertains to weightgain, development of insulin resistance and irreversible progression todiabetes. Each of the modules and submodules is described in more detailbelow.

1.0 Blood Module

In various embodiments, the blood module can be configured to receive,transport, and deliver macronutrients, lipids, insulin, hormones(leptin, ghrelin, glucagon, testosterone, estrogen, etc.),cell-signaling proteins (adipokines, cytokines, etc.), immune cells(leukocytes, lymphocytes, etc.), neurotransmitters (epinephrine,norepinephrine, acetylcholine, etc.), and the like, between the othermodules described below. An example embodiment of a generic blood moduleis illustrated in FIG. 6 .

In some embodiments, the blood module does not contain any internaldynamics but is merely responsible for the communication of theconcentration of various substances between the various other modules.

In some embodiments, the blood module can relate the dynamics of themodel to clinical measurements. For example, one way the health of aperson is measured is by drawing blood in a lab and measuring itscontent of lipids, hormones, glucose, etc. The blood module can providefor a virtual blood draw to be generated from the model and can be usedto calibrate the model to clinical blood sample data.

As discussed herein the blood module can be connected to various othermodules, including the metabolism module, the insulin production module,the insulin sensitivity module, and/or any other modules that may beincluded in the model. In various embodiments, the blood component alsoreceives data regarding consumed dietary macronutrients and distributessuch data to the appropriate modules.

One specific implementation of the blood module is illustrated in FIG. 6. In this implementation, the blood compartment is central to thesimulation and connects the remaining modules and sub-modules. FIG. 6illustrates the inputs and calculations of each of the blood levels thatmay be estimated over time. Inputs into the blood compartment modelinclude:

Muscle Glucose Free Fatty Acids (FFA) Glycerol Free amino acids LiverGlucose Glycerol Triglycerides Adipose Tissue Free Fatty AcidsCarbohydrate intake Fat intake Protein intake

The blood compartment integrates and calculates an estimate, over time,of the following concentrations in blood:

HbA1c Serum Glucose Free amino acids Chylomicrons Triglycerides FreeFatty Acids (FFA) Glycerol Ketone bodies

The blood compartment also interfaces with the terms related to theingestion of food and delivery of macronutrients to the blood supply.For steady-state behavior, the process of ingestion, digestion andabsorption of food has been abstracted into direct delivery of therelevant forms of the macronutrients in the blood compartment:

1. Carbohydrate consumption is represented as a flux of glucose intoblood.

2. Fat consumption is represented as a flux of chylomicrons into blood.

3. Protein consumption is represented as a flux of amino acids intoblood.

Transport of glucose from blood to tissues (muscle and liver) arerepresented as the function ƒ1 and ƒ2. The transport of most species arerepresented as 1st order processes which are represented as functions.The process of glycosylation of hemoglobin is incorporated in blood. Theglycosylation process (ƒ3) includes an amount of hemoglobin that can beglycosylated, which produces an upper bound on the process andconcentration of glycosylated hemoglobin (HbA1c). Detailed descriptionsof the functions in the blood module are provided below.

TABLE 1.0 Reaction Equation f₁ −((k_(glu) _(b,muscle) _(,GLUT1) *C_(GLUT1) + k_(glu) _(b,muscle) _(,GLUT4) * h(C_(GLUT4))) * (C_(glu,b) −C_(glu,muscle)))/(V_(b) + V_(ECF)) f₂ −((k_(glu) _(b,liver) _(,GLUT1) *C_(GLUT1) + k_(glu) _(b,liver) _(,GLUT4) * h(C_(GLUT4))) *C_(glu,b))/(V_(b) + V_(ECF)) f₃ k_(glu,hba1c) * C_(glu,b) *(C_(hba1c)_max − C_(hba1c,b)) f₄ −(k_(aa) _(b,muscle) * C_(aa,b) +k_(aa) _(b,liver) * C_(aa,b))/V_(b) f₅ −(k_(chy) _(lpa,muscle) *C_(chy,b) + k_(chy) _(lpa,adipose) * C_(chy,b))/(V_(b) + V_(ECF)) f₆−(k_(tg) _(lpa,aipose) * C_(tg,b) + k_(tg) _(lpa,muscle)C_(tg,b))/(V_(b) + V_(ECF)) f₇ −(k_(FFA) _(b,muscle) *C_(FFA,b))/(V_(b) + V_(ECF)) f₈ −(k_(glc) _(b,liver) * C_(glc,b) +k_(glc) _(b,muscle) * C_(glc,b))/(V_(b) + V_(ECF)) f₉ (k_(aa)_(muscle,b) * C_(aa,muscle))/V_(b) h(X)$\frac{d}{1 + {ae^{{- c} \star {({X - b})}}}}$

2.0 Metabolism Module

In one embodiment, the metabolism module is based on a metabolic modeldescribed by Kevin Hall in, “Predicting metabolic adaptation, bodyweight change, and energy intake in humans,” Am. J. Physiol. Endocrinol.Metab., 298:E449, 2010, which is hereby incorporated by reference hereinin its entirety for all purposes. For example, Hall derives adifferential equation based model that tracks the time evolution ofstored body protein (P), fat (F), glycogen (G) and extracellular fluid(ECF) in response to diet, exercise and current body weight (BW). Insupport of tracking these macronutrient variables, the metabolic modelhas explicit representations of: resting metabolic rate; total energyexpenditure; gluconeogenesis; glycogenesis; glycogenolysis; ketogenesis;lipolysis; re-esterification; de novo lipogenesis; proteolysis;glycolysis; and macronutrient oxidation rates.

In some embodiments, the metabolism module is subdivided into muscle(2.1), liver (2.2), and adipose tissue (2.3) sub-compartments. In suchembodiments, each sub-compartment can perform parallel metabolismcalculations containing different collections of processes mentionedabove (e.g., macronutrient oxidation rates may only be present in themuscle tissue in some embodiments, or the like). The sub-metabolismmodels in each metabolism sub-compartment can be specialized bycontaining different parameters to represent the differences in thesub-compartment metabolic processes (e.g., gluconeogenesis happensprimarily in the liver while macronutrient oxidation happens primarilyin the muscle, etc.). In various embodiments, each sub-component of themetabolism module component can exchange glucose data, free fatty acids(FFA) data, ketone bodies data, and protein data back and forth with theblood module (1.0).

In some embodiments, such a metabolism model can be beneficial becausethere are only a few parameters to be fit to have the model represent anindividual. Specifically, the model requires initial conditions for P,F, G, ECF, and BW, and remaining parameters can be fit to the followingdata: baseline fat intake; baseline protein intake; baselinecarbohydrate intake; and basal metabolic rate (BMR).

2.1 Metabolism Module—Muscle Submodule

The muscle compartment represents an abstraction of all major tissues inthe body other than liver and adipose tissue, which have beendistinguished in the model as their own separate compartments. Themuscle compartment incorporates the transport of all 3 majormacronutrients from blood e.g. uptake of free fatty acids (FFA) fromchylomicrons and triglycerides (ƒ 18), as shown in FIG. 7 . Thecompartment further represents the metabolic processes for theinter-conversion between different species e.g. de novo lipogenesis(DNL) (ƒ 11), glycogenesis (ƒ 14), glycogenolysis (ƒ 15), lipolysis (ƒ13). The compartment incorporates the oxidation of macronutrients,phosphorylation of adenosine diphosphate (ADP) (ƒ 16) to adenosinetriphosphate (ATP) and the hydrolysis of ATP (ƒ 17) to release energy.The model assumes that the concentration of species in the interstitialspace (ISF) and inside the cell is in rapid equilibrium. Henceconcentrations represent the concentrations of species inside the cellsin many cases. The details of the specific processes are below.

GLUT1/GLUT4 Mediated Glucose Transport

The transport of glucose into the cells of the muscle compartment isrepresented as a simple gradient flow of glucose. However, thepermeability of the gradient process is defined as a non-linear function(ƒ 10) of the concentrations of both GLUT1 and GLUT4 transporters on thecell surface. GLUT1 and GLUT4 are described in the insulin resistancemodule.

De Novo Lipogenesis (DNL)

While the liver is the main site for DNL, in conditions of excessglucose inside the muscle compartment, there is conversion to fat inthis compartment as well. The conversion of glucose to fat is modeled asa Hill function (ƒ 11) such that significant lipogenesis only happens inconditions of significant excess of glucose over steady-stateconditions.

Glycogenesis/Glycogenolysis

The model represents the dynamic equilibrium between glucose andglycogen in the muscle compartment. In the model, glycogen is made fromfour glucose molecules and this stoichiometry is maintained everywhere.Any excess glucose is converted to glycogen and vice versa. The rate ofglycogenesis (ƒ 14) is proportional to the concentration of glucose inthe tissue, insulin sensitivity (IS) and the remaining availability forglycogen storage. There is a maximum amount of glycogen that musclecells can store which is incorporated into the model and this termconstrains the glycogen concentration to that maximum amount. Theglycogenolysis process (ƒ 15) is proportional to glycogen concentrationbut is further controlled by the energy state of the muscle cellsrepresented in this term as the ATP/ADP ratio.

Fatty Acid Uptake

Serum TGs and chylomicrons serve as the primary sources of fat to themuscle compartment. Lipoprotein lipase (LPA) hydrolyses circulating TGsand chylomicrons (ƒ 18) into FFAs and glycerol in the musclecompartment. Excess glycerol transport back into blood is represented asa first order process. There is bidirectional transport of FFAs betweenblood and muscle compartment. There exists a dynamic equilibrium betweenFFA, glycerol and TG (ƒ 12, ƒ 13) where 3 FFA molecules combine with oneglycerol molecule to create one TG molecule, and this stoichiometry ispreserved everywhere.

Amino Acid Uptake and Protein Metabolism

The detailed mechanistic regulation of the amount of protein mass inhumans is not well understood in the literature. The amino acid fluxesin and out of the muscle compartment are represented as 1st orderprocesses. Inside the muscle compartment amino acid and proteinconcentrations are in dynamic equilibrium with a stoichiometric ratio of500. Amino acids are also converted into ketoacids for use in energymetabolism.

Macronutrient Oxidation and ATP Hydrolysis

The muscle compartment is the main site of macronutrient oxidation andATP hydrolysis. Glucose, fat, and ketoacids fuel the ATP synthesisprocess in the model. The described manifestation of the model onlyrepresents aerobic oxidation of the fuels and abstracts the details ofcellular respiration into a single step reaction. The rate of oxidationof all three types of fuel is proportional to the concentration of ADPand mitochondrial function (input from IR compartment). The rate ofoxidation of ketoacids is directly proportional to the concentration ofketoacids in the compartments while the rates of oxidation of glucoseand FFA are represented as saturable processes. The three fuels lead tothe generation of different amounts of ATP and this stoichiometry isalso incorporated into the equations. The stoichiometry is tuned suchthat the respiratory quotient is 0.85 at steady state and ketoacidscontribute approximately 20% to ATP synthesis.

The hydrolysis of ATP to ADP is proportional to available concentrationof ATP in the muscle compartment. However, several additional mechanismsregulate the rate of hydrolysis. The rate of ATP hydrolysis is increasedwith increase in fat mass (FM) and fat-free mass (FFM) and in the modelthe differential increase in metabolic rate is taken into account. Themodel differentiates between energy expenditure for regular bodyfunction and physical activity, so changes in energy expenditure due tochanges in physical activity are also incorporated into the ATPhydrolysis equation. Furthermore, depending upon fat content of the fatcells—leptin mediated metabolic adaptation is simulated. All of theabove mentioned factors are taken into account into the rate of ATPhydrolysis (ƒ 17).

Detailed descriptions of the functions in the muscle submodule areprovided below.

TABLE 2.0 Reac- tion Equation h(X)$\frac{d}{1 + {ae^{{- c} \star {({X - b})}}}}$ f₁₀ −((k_(glu)_(b,muscle) _(,GLUT1) * C_(GLUT1) + k_(glu) _(b,muscle) _(,GLUT4) *h(C_(GLUT4))) * (C_(glu,b) − C_(glu,muscle)))/(V_(muscle)) f₁₁$k_{{glu},{ffa}_{muscle}} \star \frac{C_{{glu},t}^{n_{{glu},{ffa}}}}{C_{{glu},t}^{n_{{glu},{ffa}}} + {km_{{glu},{ffa}}^{n_{{glu},{ffa}}}}}$f₁₂ k_(ffa,tg) _(muscle) * C_(ffa,t) * C_(glc,t) f₁₃$\frac{\left( {k_{{tg},{ffa}_{muscle}} \star C_{{tg},t}} \right)}{\left( {1 + \left( \frac{IS}{kI_{{lipolysis},{insulin}}} \right)^{n_{{lipoysis},{insulin}}}} \right)}$f₁₄ k_(glu,gly) _(muscle) * (C_(gly,muscle) ^(max) − C_(gly,t)) *C_(glu,t) * IS f₁₅$k_{{gly},{glu}_{muscle}} \star \frac{C_{{gly},t}}{1 + \left( \frac{r_{{ATP},{ADP}}}{kI_{{dg},{atp}}} \right)^{n_{{dg},{atp}}}}$f₁₆ $\begin{pmatrix}{{k_{{ffa},{adp}} \star \frac{C_{{ffa},t}}{C_{{ffa},t} + {km_{{ffa},{adp}}}}} + {k_{{ketoa},{adp}} \star C_{{ketoa},t}} +} \\{k_{{glu},{adp}} \star \frac{C_{{glu},t}}{C_{{glu},t} + {km_{{glu},{adp}}}}}\end{pmatrix} \star C_{{adp},t} \star C_{mito}$ f₁₇ k_(atp,adp) *C_(atp,t) * (1 + α_(RER,FFM) * (FFM − FFM₀) + α_(RER,FM) * (FM −FM₀)) *RMR_(PAadjustment) * RMR_(BWadaption) f₁₈ (k_(tg,lpa) _(muscle) *C_(tg,b) + k_(chy,lpa) _(muscle) C_(chy,b)) * (1 +α_(lpa,ampk))/(V_(muscle)) mROS k_(etc,leakage)f₁₆

2.2 Metabolism Module—Liver Submodule

Liver is another major compartment involved in macronutrient metabolism.Many of the processes incorporated in the muscle compartment are alsopresent in the liver compartment (FIG. 8 ) and only the differences havebeen described in detail. FIG. 8 illustrates the inputs and calculationsof each of the blood levels that may be estimated over time. Inputs intothe liver compartment model include:

Serum Glucose Free Fatty Acids (FFA) Glycerol GLUT1/GLUT4 IS KetoneBodies

GLUT1/GLUT4 and IS are described in in the insulin resistance module.

The liver compartment integrates and calculates an estimate, over time,of the concentrations of following in liver:

Glycogen Liver Glucose Free Fatty Acids (FFA) Triglycerides GlycerolKetone bodies

GLUT1/GLUT4 Mediated Glucose Transport

The mechanism of glucose uptake by the liver compartment (ƒ20) follows asimilar mechanism as that in the muscle compartment, but in the liver,the influx depends only on the concentration of glucose in the bloodrather than the glucose gradient, and there also exists a flux ofglucose out of the tissue.

De Novo Lipogenesis (DNL)

The liver is the primary site for DNL in the body. The expression forDNL (ƒ23) in liver is the same as that in the muscle compartment onlyhere it depends on the concentration of glucose in the liver.

Glycogenesis/Glycogenolysis

Glycogen-glucose dynamics in the liver compartment are represented thesame way as in the muscle compartment. During rigorous physicalactivity, liver glycogen is preferentially used as a source of glucose.To reproduce this specific behavior, an additional mechanism has beenadded in the model to enhance the glycogenolysis during physicalactivity (ƒ21).

Gluconeogenesis from Amino Acids and Glycerol

The liver compartment is the site of gluconeogenesis. The modelincorporates gluconeogenesis from glycerol (ƒ24) as well from ketoacids(ƒ30) and they are differentially regulated. Gluconeogenesis fromketoacids is assumed to be proportional to the concentration ofketoacids in the liver. However, gluconeogenesis from glycerol isinhibited by increased insulin sensitivity (IS) (input from the insulinresistance module).

Fatty Acid Metabolism

Unlike in the muscle, in the liver, there is no uptake of FFA from TGsand chylomicrons. There is uptake of FFAs from blood into liver thatfollows a 1st order process. The new FFA that is synthesized through DNL(ƒ23) and FFA absorbed from blood are combined with glycerol to form TG(ƒ27) that is transported out.

Ketogenesis

The model also incorporates a simplified representation of theketogenesis (ƒ26) that increases significantly when there is excess FFAin the liver compartment. Some of the ketone bodies are reconverted toFFA (ƒ25) while the remaining is transported to blood over a gradient.

Detailed descriptions of the functions in the liver submodule areprovided below.

TABLE 3.0 Reaction Equation h(X)$\frac{d}{1 + {ae^{{- c} \star {({X - b})}}}}$ f₁₉$\frac{C_{{ADP},t}}{C_{{ATP},{total}} - C_{{ADP},t}}$ f₂₀ ((k_(glu)_(b,liver,glut1) * C_(GLUT1) + k_(glu) _(b,liver,glut4) * h(GLUT4)) *C_(glu,b))/ (V_(liver)) f₂₁$k_{{gly},{glu}_{liver}} \star C_{{gly},t} \star \frac{1 + {\alpha_{{glycogenolysis},{liver}_{PA}} \star \left( {e^{\alpha_{{epi},{PA}} \star f_{PA}} - 1} \right)}}{1 + \left( \frac{r_{{ATP},{ADP}}}{kI_{{dg},{atp}}} \right)^{n_{{dg},{atp}}}}$f₂₂ k_(glu,gly) _(liver) * (C_(gly,liver) ^(max) − C_(gly,t)) *C_(glu,t) * IS f₂₃$k_{{glu},{ffa_{liver}}} \star \frac{C_{{glu},t}^{n_{{glu},{ffa}}}}{C_{{glu},t}^{n_{{glu},{ffa}}} + {km_{{glu},{ffa}}^{n_{{glu},{ffa}}}}}$f₂₄$k_{{glc},{glu}} \star \frac{C_{{glc},t}}{1 + \frac{IS}{kI_{{gngf},{insulin}}}}$f₂₅ k_(kb,ffa) * C_(kb,t) f₂₆$k_{{ffa},{kb}} \star \frac{C_{{ffa},t}^{n_{{ffa},{kb}}}}{C_{{ffa},t}^{n_{{ffa},{kb}}} + {km_{{ffa},{kb}}^{n_{{ffa},{kb}}}}}$f₂₇ k_(ffa,tg) _(liver) * C_(ffa,t) * C_(glc,t) f₂₈ k_(p,aa) _(liver) *C_(p,t) f₂₉ k_(aa,p) _(liver) * C_(aa,t) f₃₀ k_(ketoa,glu) _(liver) *C_(ketoa,t)

2.3 Metabolism Module—Adipose Tissue Submodule

The adipose compartment represents the site of both visceral andsubcutaneous fat tissue. The adipose compartment incorporates uptake offat from blood, and the dynamic equilibrium between lipolysis andesterification. The fatty acid metabolism shown in FIG. 9 is the same asdescribed in the muscle compartment, only adipose tissue absorbs moreFFA from circulating TG and chylomicrons than muscle. Briefly,chylomicrons and triglycerides in blood are hydrolyzed to form FFA andglycerol which is absorbed into adipose tissue (ƒ31). Glycerol is alsosynthesized in the adipose tissue. Some of the glycerol and FFA arere-esterified to form triglycerides which are stored in the adiposetissue (ƒ32). The remaining is released back into blood.

During vigorous physical activity there is significantly increasedlipolysis that is predominantly localized to the adipose tissue. Hencein the model, lipolysis in the adipose tissue is increased by exercise.The model also incorporates the regulation of lipolysis byinsulin—increased insulin sensitivity decreases the rate of lipolysis(ƒ33). When the fat content per fat cell—defined as triglyceride per fatcell increases above the baseline value, it drives the synthesis of newfat cells (ƒ34).

Inputs into the adipose compartment model include:

Triglycerides Free Fatty Acids (FFA) Glycerol Chylomicrons IS

The adipose compartment integrates and calculates the followingvariables:

Free Fatty Acids (FFA) Glycerol Triglycerides Adipocyte count

Detailed descriptions of the functions in the liver submodule areprovided below.

TABLE 4.0 Reaction Equation f₃₁ (k_(chy) _(lpa,adipose) * C_(chy,b) +(k_(tg) _(lpa,adipose) * C_(tg,b))/V_(adipose) f₃₂ (k_(FFA)_(tg,adipose) * C_(ffa,t) * C_(glc,t))/V_(adipose) f₃₃$\frac{k_{tg_{{ffa},{adipose}}} \star C_{{tg},t} \star k_{{lipolysis},{PA}}}{1 + \left( \frac{IS}{kI_{{lipolysis},{insulin}}} \right)^{n_{{lipolysis},{insulin}}}}$f₃₄ C_(tg,t)/N_(ap) k_(lipolysis,PA)$1 + {\alpha_{{lipolysis}_{PA}}\left( \frac{{percent\_ v02}\max^{n_{{lipolysis}_{{PA},{adipose}}}}}{\left( {{{percent\_ v02}\max^{n_{{lipolysis}_{{PA},{adipose}}}}} + K_{m,{lipolysis}_{{PA},{adipose}}}^{n_{{lipolysis}_{{PA},{adipose}}}}} \right.} \right)}$

3.0 Insulin Resistance Module

In the specific embodiments illustrated in FIGS. 10A and 10B, theinsulin resistance (IR) module represents processes that modulate thefollowing: a) the response of cells to insulin, b) short and long-termregulation of mitochondrial function, and c) the effects of energydepletion on AMPK activity.

Insulin (input from the insulin production module) binds (ƒ35, ƒ36) tothe transmembrane insulin receptors (InsR) through a reversiblereaction. The bound complex (InsR-Insulin) undergoes conformationalchanges to form the active form of the complex (InsRA). Reactive oxygenspecies (mROS) (input from the muscle compartment and described in Table2) and serum FFA (input from the blood component) have been shown toincrease the natural rate of dephosphorylation of the insulin-insulinreceptor complex (ƒ37, ƒ38, ƒ39). Since the dynamics of insulin receptorphosphorylation and dephosphorylation is significantly faster than thedynamics of insulin, FFA etc., a quasi-steady state approximation wasused to convert the differential equations for the two forms of thereceptors into algebraic equations. Insulin sensitivity is expressed asthe fraction of insulin receptors which are in the phosphorylated(active) state (InsRA), further normalized by the level of InsRA atsteady-state and expressed as IS (ƒ40).

The concentrations of glucose transporters (GLUT1 and GLUT4) on the cellsurface regulate the permeability of glucose transport into cells asillustrated in FIG. 10B. The model represents the dynamics of GLUT1 andGLUT4 concentrations on the cell surface as well as the intracellularconcentrations. Both transporters undergo endocytosis through a firstorder process. Both GLUT1 and GLUT4 transport to the cell surface byregular transport (ƒ41, ƒ44) but also controlled (ƒ42, ƒ45) by changinginsulin sensitivity (IS). However, GLUT4 transport is highly responsiveto increases in insulin concentrations but not GLUT1. GLUT4 transport tothe cell surface is further increased by AMPK activity (ƒ47). Inside thecell, GLUT1 transporters (GSC1) is synthesized at a constant rate anddegraded using a first order process. Intracellular GLUT4 (GSC4) iscontrolled by the same mechanism. Both GLUT1 and GLUT4 undergoendocytosis (ƒ43, ƒ46) to bring it back inside the cell.

Mitochondrial function (half-life of 3 days) controls macronutrientoxidation, thereby controlling the rate of generation of ATP. ATPdeficiency (or ADP excess) leads to upregulation of AMPK activity thatin turn increases the synthesis of mitochondria. In the model,concentration of adenosine monophosphate (AMP) is assumed to beproportional to that of ADP. ADP concentration controls the AMPKactivity through a Hill function (ƒ48). Mitochondrial synthesis in themodel is directly proportional to AMPK activity. Age-dependentcumulative ROS leads to increased rate of degradation of mitochondria.The instantaneous mROS level is a function of macronutrient oxidation(input from the muscle component) which is integrated into thecumulative term for ROS. The accumulated ROS drives the degradation ofmitochondria (ƒ49).

Inputs into the insulin resistance compartment model include:

Serum insulin Free Fatty Acids (FFA) mROS from muscle compartment ADPfrom muscle compartment

The insulin resistance compartment integrates and calculates anestimate, over time, of the following parameters:

Insulin sensitivity (IS) GSC1 GSC4 Mitochondrial function accumulatedROS GLUT4 GLUT1

Detailed descriptions of the functions in the liver submodule areprovided below.

TABLE 5.0 f₃₅ k_(on)C_(insulin,b)C_(insR) − k_(off)C_(insR−insulin) f₃₆k_(recycle)C_(insR−insulin) f₃₇ k_(auto)C_(insRA) f₃₈$k_{{ri_{inact}},{ffa}}C_{insRA}\frac{C_{{ffa},{muscle}}^{n}}{C_{{ffa},{muscle}}^{n} + {EC50_{{ffa},{muscle}}^{n}}}$f₃₉ k_(ri) _(inact) _(,ROS)C_(insRA)C_(ROS) v₁$\left( {\frac{\left( {k_{off} + k_{ract}} \right)}{\left( {{Kd_{{insulin}R}} \star \left( {f_{37} + f_{38} + f_{39}} \right)} \right)} - \frac{k_{on}}{\left( {f_{37_{ss}} + f_{38_{ss}} + f_{39_{ss}}} \right)}} \right)C_{{insulin},b}$v₂ C_(insulin,b)/Kd_(insulinR) f₄₀$\frac{v_{1} \star C_{insR}^{total}}{\left( {1 + v_{1} + v_{2}} \right)}/\frac{v_{1_{ss}} \star C_{insR}^{total}}{\left( {1 + v_{1_{ss}} + v_{2_{ss}}} \right)}$f₄₁ k_(recycle,GSC1)C_(GSC1) f₄₂k_(recycle,GSC1,insulin)C_(GSC1)C_(insRA) f₄₃k_(internalization,GLUT1)C_(GLUT1) f₄₄ k_(recycle,GSC4)C_(GSC4) f₄₅k_(recycle,GSC4,insulin)C_(GSC4)C_(insRA) f₄₆k_(internalization,GLUT4)C_(GLUT4) f₄₇k_(recycle,GSC1,insulin)C_(GSC1)C_(AMPK) f₄₈$k_{{AMPK},{ADP}}\frac{C_{ADP}^{n_{{AMPK},{ADP}}}}{C_{ADP}^{n_{{AMPK},{ADP}}} + {Km}_{{AMPK},{ADP}}^{n_{{AMPK},{ADP}}}}$f₄₉ d_(mito,ROS)C_(mito)C_(AUCROS)

4.0 Insulin Production Module (Pancreas)

In various embodiments, the insulin production module can comprise amodel of the pancreas described in De Gaetano et al. “Mathematicalmodels of diabetes progression,” Am. J. Physiol. Endocrinol. Metab.,295:E1462, 2008, which includes the production of insulin by thepancreas as a function of plasma glucose levels and active pancreaticbeta cells. This paper is hereby incorporated herein by reference in itsentirety for all purposes.

For example, insulin is produced by pancreatic beta cells as a functionof blood glucose and fat levels and active beta cells. The number ofactive beta cells increases with increasing levels of blood glucose.Chronic inflammation and oxidative stress due reactive oxygen species(ROS), generated as a result of over-nutrition or other factors, damagesbeta cells and accelerates apoptosis. Some beta cells spontaneouslyrecover from this damage, but conditions such as chronic hyperglycemialead to steadily decreasing pancreatic capacity and hence reducedinsulin production. This reduction in insulin production is one of thedriving factors in development of diabetes.

Accordingly, in various embodiments, the insulin production module canbe configured to estimate an individual's current pancreatic reserve ofbeta cells and predict amount of insulin produced in the pancreas. Invarious embodiments the model can account for growth of beta cells anddamage done to them by reactive molecules.

The insulin production module may be coupled with other modules invarious suitable ways. For example, the glucose and free fatty acidsdata (i.e., present in the blood) may be provided by the blood module asan input to the insulin production module. The insulin production modulealso may provide insulin data to the blood module, and such insulin datacan be communicated to the insulin sensitivity module where glucoseuptake can be regulated. The insulin production module also may provideinsulin data to the blood module 231, and such insulin data then may becommunicated to the metabolism module where such insulin data mayinfluence estimations of gluconeogenesis, proteolysis, lipolysis,esterification, glycogenesis, and the like.

The presently-described pancreas model is a modification of that of DeGaetano et al. (2008). The model incorporates the long-term dynamics ofbeta cell mass, beta cell function and insulin production as shown inFIG. 11 . Proliferation of beta cells is represented as a Hill functiondependent on blood glucose (input from blood component) concentration(ƒ50). Beta cell apoptosis is a function of chronic inflammation in theform of ROS. ROS in this module is represented as being generated bycombination of serum glucose and serum FFA in excess of their normallevels respectively (ΔC_(ROS)). Beta cell function bounded between 0 and1 is decreased at a rate proportional to remaining beta cell functionand ROS concentrations (ƒ52). The beta cell function recovers followinga 1st order process.

The rate of insulin production is proportional to the beta cell number,beta cell functional capacity (1−beta cell damage) and product of a Hillfunction (ƒ51) of serum glucose concentration (R_(insulin,glu)) and aseparate Hill function (ƒ52) of serum FFA concentration(R_(insulin, ffa)). Insulin is removed from blood following a 1st orderprocess.

Inputs into the insulin production compartment model include serumglucose and serum FFA. The insulin production compartment integrates andcalculates an estimate, over time, of pancreatic beta cell count, betacell damage, and serum insulin concentration.

Detailed descriptions of the functions in the liver submodule areprovided below.

TABLE 6.0 f₅₀$k_{{betacells},{glu}}\frac{C_{{glu},b}^{n_{{betacells},{glu}}}}{C_{{glu},b}^{n_{{betacells},{glu}}} + {Km_{{betacells},{glu}}^{n_{{betacells},{glu}}}}}$f₅₁$k_{{insulin},{glu}}\frac{C_{{glu},b}^{n_{{betacells},{glu}}}}{C_{{glu},b}^{n_{{betacells},{glu}}} + {Km_{{betacells},{glu}}^{n_{{insulin},{glu}}}}}$f₅₂$\frac{(1)}{\left( {1 + \left( \frac{C_{{ffa},b}}{kI_{{insulin},{ffa}}} \right)^{n_{{insulin},{ffa}}}} \right)}$ΔC_(ROS)${k_{{ros},{glu}}\left( {C_{{glu},b} - C_{{glu},b_{ss}}} \right)} + {k_{{ros},{ffa}}\frac{\left( {C_{{ffa},b} - C_{{ffa},b_{ss}}} \right)}{\left( {C_{{ffa},b} - C_{{ffa},b_{ss}}} \right) + {Km_{{ros},{glu}}}}}$f₅₃ k_(bc) _(damage) _(,ROS)ΔC_(ROS)(1 − C_(bc) _(damage) )

Calculation of Weight:

Weight of the whole body is calculated based on concentrations ofglycogen, protein and fat. The water associated with thesemacronutrients is also taken into account.

TABLE 7.0 Glycogen C_(glycogen,liver)V_(liver) +C_(glycogen,muscle)V_(muscle) Protein C_(protein,liver)V_(liver) +C_(protein,muscle)V_(muscle) Fat C_(TG,liver)V_(liver) +C_(TG,muscle)V_(muscle) + C_(TG,adipose)V_(adipose) FFM(hydration_(fat)Fat + 0.25 * Weight_(ss) + Protein +Glycogne)/hydration_(ffm) Weight FFM + Fat

Use Case 1: Population Study

The goal of this study was to predict the effect oflifestyle-interventions on pre-diabetic patients based on their timecourse of weight and HbA1c measurements and the onset of diabetes.

We were granted access to the publicly released DPP data through theNational Institute of Diabetes and Digestive and Kidney Diseases(NIDDK). We examined data collected from study participants throughbaseline, quarterly, mid-year, and annual medical visits over threeyears of the DPP study in order to have a sufficient number of patientsfor time-course analysis. The variables extracted, including patientidentifiers, demographic information, and clinical measurements.

The computational model of diabetes was used to calibrate to the timecourse data of placebo subjects in the DPP study. The age, height andgender of each patient were provided as input, while time course ofweight (Kg) (W), HbA1c (%), fasting serum glucose (mM) and fasting seruminsulin (pM) were used to calibrate the model. From initial review itwas observed that if the diet reported in the study was directly used asan input to the model, it made it extremely difficult for the model tomatch the time course of measurements for the above-mentioned variablesduring the study. Previous studies have shown that diet reporting isoften unreliable, so we accounted for incorrect diet reporting by usingcorrection factors for the diet both before and during the study. Therewere 12 parameters that were varied on an individual patient basis tomatch the experimental data.

The parameters were estimated by using weighted least squares estimationusing the differential evolution algorithm. The form of the objectivefunction was:

${\Phi(\theta)} = {\sum\limits_{i}{\sum\limits_{j}{w_{ij}\left( {y_{ij} - y_{ij}^{*}} \right)}^{2}}}$

i=[W,C_(HbA1c) ^(B),C_(G) ^(B),C₁ ^(B)] and j=1,n_(i) where n_(i) is thenumber of data point available for the i^(th) variable during the study.w_(ij) represents the weight for the i^(th) variable at the j^(th) timepoint.

Baseline Calibration Data

In various embodiments, the baseline calibration of the model serves asan initial starting point for further calibration to specificindividuals and populations. For example, to calculate the baselinecalibration of the model to the general US population, various publiclyavailable data sets are used. One such data set is the National Healthand Nutrition Examination Survey (NHANES) data set. NHANES is anationally commissioned health survey sponsored by the Center forDisease Control and Prevention (CDC) to record and publish individualhealth conditions over time. A model can be calibrated to a specificdemographic within the NHANES dataset using a Synthetic Panel Data (SPD)method, or the like. SPD can be used to approximate longitudinal datafrom non-longitudinal data sets. The SPD method works by identifying anage cohort of interest and then shifts the age of that cohort to takethe progression of time.

For example FIGS. 12A-D and 13A-C show bar graphs of the body weight forwomen initially between the ages of 50-55 starting in 1999 and then in2001, 2003, 2005, 2007, 2009 and 2011 respectively. The NHANES data setis tracked through time using the SPD method. Once the time progressionof demographic measurement has been extracted using SPD, a time seriesof that measurement can be generated for use in calibrating the model.For example, FIG. 14 illustrates a set of calibration data 1600 overtime.

In several embodiments, other data available from sources like publishedscientific literature, electronic health records, prescription data,claims data, and the like could also be used similarly for baselinecalibration.

Calibration of Diabetes Model to Patients from the LifestyleIntervention Arm

The computational model of diabetes was calibrated to the time course ofa small set of patients from the Lifestyle intervention arm of the DPPstudy. The same patient characteristics (e.g., age, gender etc.) andtime course (e.g., weight, HbA1c etc.) were used for the calibration.

To accurately model and simulate health outcomes to match known data atindividual or population levels, various calibration methodologies canbe used to train the model to data of interest. In some embodiments, thecalibration process utilizes an Approximate Bayesian Computation methodto non-parametrically estimate the posterior parameters distribution. Insome embodiments, a large number of parameters may need to be estimated,so a Markov Chain Monte Carlo can be used to find parameter values tosimulate observed population level statistics.

In some embodiments, the calibration process utilizes evolutionaryoptimization algorithm. Evolutionary algorithms are a class ofstochastic, global optimization algorithms that can follow heuristicsbased on the process of biological evolution of a population. Forexample, the calibration process could use differential evolution, atype of evolutionary algorithm that searches for the optimum solutionusing a population of candidate solutions.

In various embodiments other known local or global optimizationalgorithms could be used either in isolation or in combination, assuitable for the calibration problem.

In various embodiments, a given model can use a combination of userspecified initial conditions and/or inputs over time to predict modeloutputs. The calibration process can generate a set of acceptableparameters to match the model's simulated outputs to the observed datafrom the individual or population of interest. For example, Table 1below illustrates the four example types of values in the calibrationprocess of one embodiment.

Type of Value Description Example Initial Data about 1. Demographic dataabout gender, Conditions condition of ethnicity and current height andage individual/ 2. Behavioral information including, population at forexample, physical activity (e.g., simulation calories burned duringexercise or start time other activity), alcohol consumption, and diet(e.g., kcals obtained from fat, carbs and proteins, and the like) 3.Serum HbAlc concentration 4. Current Insulin Resistance ParametersFactors internal Resting metabolic rate to the model, Excess caloricintake varies by EC50(FFA) individual/ Beta-cell apoptosis ratepopulation Inputs Time varying 1. Diet (e.g, kcals obtained from fat,information carbs and proteins, and the like) used by 2. Physicalactivity (e.g., calories model to burned during exercise or othercalculate activity) outputs. Outputs Model 1. Predicted Weightforecasted 2. Predicted Insulin Resistance variable 3. Predictedmetabolic and/or that evolve physiological parameters (e.g., over timeserum HbA1c concentration, serum glucose, serum insulin)

Using the same strategy as the calibration of placebo patients, theunder-reporting of the diets by the Lifestyle intervention arm subjectswas taken into account by adding additional parameters to estimate theamount of underreporting during the pre-study and study periods. Toprevent the addition of too many free parameters, the change in physicalactivity that is significant for this arm is directly determined by thedocumented physical activity, assuming it was reported accurately.

The reported physical activity for the subjects was often found to bevarying during the study period. For the purposes of this study, weassumed a linear trend in activity over time and used a Python packageto fit a linear regression model to the activity data in order to definean intercept and slope that represents the activity of each Lifestylecohort patient during the 3 year study. The best fit patients, asdefined by, R²>0.85 were selected for subsequent selection andcalibration. Similar to the placebo population, statistical tests wereused to compare the model output to the real data from the study.

Model Validation

The goal of the study is to be able to predict the effect oflifestyle-interventions on pre-diabetic patients based on their timecourse of weight and HbA1c measurements and the onset of diabetes. TheDPP study lifestyle-intervention arm was not designed with a specificintervention that all patients were expected to follow. As a result,every patient in the study had a unique lifestyle-intervention.Individual patient parameter estimation providing the model with thepatient demographics and only the baseline measurement would lead to toomany combinations of parameters without any ability to choose betweenthem which makes it unsuitable for prediction of response tolifestyle-intervention. Hence, for the purposes of model validation thefollowing strategy was used:

Each placebo patient was assigned a unique lifestyle-intervention basedon the lifestyle-interventions observed in the real patients andsimulated.

The distribution of the simulated model output for weight and HbA1cwould be compared to the distribution of real measurements from thepatients of the lifestyle-intervention arm of the study.

The rate of onset of diabetes from the simulated patients would becompared to the rate of onset of diabetes in the real patients in thelifestyle-intervention arm of the study.

It was observed that the baseline distribution of age, weight and HbA1cof the placebo patients when compared to those of the patients in thelifestyle-intervention arm did not pass 2-sample KS test with 95%confidence level. This necessitated that a sub-sample of patients fromthe placebo patients be chosen whose baseline measurements would bestatistically similar to those of the lifestyle-intervention subjects.

The two attributes to the change in physical activity (namely interceptand slope) during the study were analyzed and found to be independent ofall patient attributes. However, intercept and slope parametersthemselves show a loose correlation (correlation coefficient=−0.43).These two parameters were thus generated randomly from the originaldistributions (preserving the correlation coefficient) and assigned toeach placebo patient.

Each placebo patient was then compared to the 60 patients from thelifestyle-intervention arm used in calibration using nearest neighboranalysis. The patient attributes used in the computation of nearestneighbor were—patient age and 6 patient parameters namely (metabolicneed for carbohydrate and fat, consumption of carbohydrate and fatbefore the study and consumption during the study). The parameters werenormalized using Z-score method and Euclidian distance metric was usedas described below.δ_(i)=(θ_(i)−θ _(i))/σ_(i)d=√{square root over (Σ(δ_(i)−δ_(i)*)²)}

θ_(i) represents the i^(th) patient attribute, θ_(i) and σ_(i) representthe mean and standard deviation of the attribute across the populationof patients; δi represents the normalized attribute; δi* represents thenormalized parameter value of the test placebo patient; d represents theEuclidian distance of the test patient compared to a single patient fromthe population of patients used in calibration of lifestyle-interventionpatients. The nearest neighbor defined by the patient with the minimumvalue of d was identified and the change in intake of carbohydrate andfat as estimated from model calibration was assigned to the placebopatient. Each placebo patient was therefore assigned all 4 parametersrelated to lifestyle change—change in physical activity (intercept andslope) and change in intake of carbohydrate and fat. These placebopatients with unique lifestyle-interventions were simulated and modeloutput compared to the real patients of the lifestyle-intervention armas described above.

Analysis of Results

Role of Parameters in Diabetes Progression

The goal of this analysis was to identify if there are statisticallysignificant differences between the estimates of specific parametersbetween the subjects who went on to develop diabetes in the 3 years fromthe start of the study in comparison to those subjects who did not. Tomaximize the difference between the parameters, the change in HbA1cduring the years of the study was collected and the patients whosechanges in HbA1c were between minimum and 1st quartile (group 1) andthose between 3rd quartile and maximum (group 2) were separated into twodifferent populations. For these 2 populations, 2-sample KS test wasperformed to compare the parameter distributions for each parameterindependently. In addition, using a binary classification group 1 being0 and group 2 being 1, the populations were processed using aclassification decision tree using scikit-learn python package and theresultant decision-tree is analyzed.

Discussion of Results

We use the computational model of diabetes to calibrate to the timecourses of 4 clinical variables for all subjects from the placebo arm asdescribed above. The model is able to accurately fit to all 4 variablessimultaneously in greater than 80% of cases. Comparison of model outputto the experimental individual subject data is done both at anindividual subject level as well as an aggregate population level.

Four examples subjects are shown (FIGS. 15A, B, C, D), which demonstratethe model's ability to match the experimental data. These four subjectswere chosen because they show very different time course. The datapoints show the time course of real measurements for the patients andthe line represents the model simulation for the same period of timebased on the calibrated parameters. The subjects in FIG. 15A shows anincrease in weight and simultaneous increase in HbA1c. However, subjectin FIG. 15B shows a decrease in weight with no real improvement in theHbA1c. FIG. 15D represents a subject that had both a reduction in weightand HbA1c levels. The model accurately predicted the dynamics in allcases.

At the aggregate population level, the histograms of experimental datafor weight and HbA1c are generated for both experimental data andsimulated model output for baseline and every year thereafter. Thesimulated model output closely matches the distribution of theexperimental data at year 3 as seen in FIGS. 16A-D.

As described above, a small subset of subjects from thelifestyle-intervention arm were used for model calibration. Thepredictions for the effect of lifestyle interventions were generated bysimulating the selection group of placebo subjects with individuallifestyle interventions as described above. Comparison of thedistributions of weight and HbA1c at year 3 between the experimentaldata and predicted model output are shown.

To further provide evidence, statistical comparison of the twodistributions is done at year 3 and shown below.

Placebo Lifestyle Baseline Year 3 intervention Year 3 Weight 0.76 0.990.15 HbA1c 0.64 0.85 0.06

There is no statistically significant difference between thedistributions based on study data and model predictions (p-value >0.05).

Use Case 2: Individual Study

To train and test the model against individual level human data, we useddata from a weight loss study by Gardner et al., in which theyrandomized 61 overweight adults to low-fat (LF) or low-carbohydrate (LC)diets for a 6-month period (Obesity (Silver Spring). 2016 January;24(1):79-86. doi: 10.1002/oby.21331. Epub 2015 Dec. 6). Data for eachsubject were recorded at baseline, 3 months and 6 months and includedself-reported diet captured via a 24-hr dietary recall, and measuredbody weight, blood glucose and fasting insulin among others. At the endof the 6 months, a diet crossover was performed so that the LF subjectsswitched to LC and vice versa and continued for another 6 months. Datawere collected at 9 and 12 months. Of the 61 participants, 41 wereselected for further analysis in this study, as they had no missing datafor diet, body weight, blood glucose, and insulin measurements. Toprovide additional training data to the model, we estimated fat mass andfat free mass from the measured body weight time course.

We selected subset of the base parameters of the model using ourunderstanding of the model structure and calibrated these parametersseparately for each individual to train the model to individual datarecorded in the weight loss study. After training the selectedparameters separately to each individual, the dynamics of change in bodyweight, fat mass, fat free mass, fasting blood glucose and fastinginsulin were captured quantitatively by the model within acceptableerror. A typical example of the model fitted to an individual is shownin FIGS. 17A and B. In order to quantify how well the “model” fit toeach individual, we calculated an aggregated error of fit for theindividuals. The aggregated error showed that the model could capturedynamics of 37 out of the 41 (˜90%) individuals within acceptablelimits.

Results—Prediction of Weight Loss

In order to validate the model, a cross-validation strategy was used inwhich the data set for each subject of the weight loss study was splitinto two complementary parts. Data set 1 corresponded to first 6 monthsof the study on one type of diet (either LC or LF). Data set 2 comprisedof data from the latter 6 months of the study, when the subjects hadswitched their diets. The model was trained using an approach similar tothat described above, but we only used data set 1 in the trainingprocess. After determining the parameters for each individual bytraining to data set 1, the last 6 months of the study was simulated andcompared the predictions with data set 2. The large majority of thepredictions fell within acceptable measurement error of the variablesand the model captured general trends of the measured values very well.A typical example of fitting to data set 1 and predicting data set 2 isillustrated in FIGS. 17 (A-B) (dotted vertical line represents the6-month mark demarcating the switch from dataset 1 to dataset 2). Inorder to assess the quality of prediction for the entire group ofsubjects, an aggregated fitness score identical to the one describedabove was used. The number of individuals that fit well was not as highas it was when the entire data set was used for training; however, themodel successfully predicted time dependent changes in the variables ofmore than 70% individuals as seen in FIG. 17 (in each figure right ofthe dotted line represents prediction).

As previously mentioned, FIG. 18 illustrates an exemplary computerarchitecture 1800 for use with the health and body simulation systemsdisclosed herein, according to one embodiment. One embodiment ofarchitecture 1800 comprises a system bus 1820 for communicatinginformation, and a processor 1810 coupled to bus 1820 for processinginformation. Architecture 1800 further comprises a random access memory(RAM) or other dynamic storage device 1825 (referred to herein as mainmemory), coupled to bus 1820 for storing information and instructions tobe executed by processor 1810. Main memory 1825 also may be used forstoring temporary variables or other intermediate information duringexecution of instructions by processor 1810. Architecture 1800 also mayinclude a read only memory (ROM) and/or other static storage device 1826coupled to bus 1820 for storing static information and instructions usedby processor 1810.

A data storage device 1827 such as a magnetic disk or optical disc andits corresponding drive may also be coupled to computer system 1800 forstoring information and instructions. Architecture 1800 can also becoupled to a second I/O bus 1850 via an I/O interface 1830. A pluralityof I/O devices may be coupled to I/O bus 1850, including a displaydevice 1843, an input device (e.g., an alphanumeric input device 1842and/or a cursor control device 1841).

The communication device 1840 allows for access to other computers(servers or clients) via a network. The communication device 1840 maycomprise one or more modems, network interface cards, wireless networkinterfaces or other well-known interface devices, such as those used forcoupling to Ethernet, token ring, or other types of networks.

In the description above, for purposes of explanation only, specificnomenclature is set forth to provide a thorough understanding of thepresent disclosure. However, it will be apparent to one skilled in theart that these specific details are not required to practice theteachings of the present disclosure.

Some portions of the detailed descriptions herein are presented in termsof algorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the below discussion, itis appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a non-transitorycomputer readable storage medium, such as, but is not limited to, anytype of disk, including floppy disks, optical disks, CD-ROMs, andmagnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any typeof media suitable for storing electronic instructions, and each coupledto a computer system bus.

The algorithms presented herein are not inherently related to anyparticular computer or other apparatus. Various general purpose systems,computer servers, or personal computers may be used with programs inaccordance with the teachings herein, or it may prove convenient toconstruct a more specialized apparatus to perform the required methodsteps. The required structure for a variety of these systems will appearfrom the description herein. It will be appreciated that a variety ofprogramming languages may be used to implement the teachings of thedisclosure as described herein.

Moreover, the various features of the representative examples and thedependent claims may be combined in ways that are not specifically andexplicitly enumerated in order to provide additional useful embodimentsof the present teachings. It is also expressly noted that all valueranges or indications of groups of entities disclose every possibleintermediate value or intermediate entity for the purpose of originaldisclosure, as well as for the purpose of restricting the claimedsubject matter. It is also expressly noted that the dimensions and theshapes of the components shown in the figures are designed to help tounderstand how the present teachings are practiced, but not intended tolimit the dimensions and the shapes shown in the examples.

The described embodiments are susceptible to various modifications andalternative forms, and specific examples thereof have been shown by wayof example in the drawings and are herein described in detail. It shouldbe understood, however, that the described embodiments are not to belimited to the particular forms or methods disclosed, but to thecontrary, the present disclosure is to cover all modifications,equivalents, and alternatives.

What is claimed is:
 1. A method for predicting a time to diabetes onsetin a subject, comprising: a. determining, for the subject, subjectparameter values comprising height, weight, age, gender, fasting bloodglucose concentration, and serum HbA1c concentration at a first time; b.calculating initial parameter set values comprising basal metabolicrate, excess caloric intake, EC50(FFA), and beta cell apoptosis ratebased on the subject parameter values by (i) setting an age estimate toan age younger than the age determined in step (a), setting a weightestimate to a weight that is different from the weight determined instep (a), setting a fasting blood glucose concentration estimate to avalue different from the fasting blood glucose concentration determinedin step (a), and setting a serum HbA1c estimate to a value differentfrom the serum HbA1c concentration determined in step (a), (ii)providing an initial estimate for each value in the initial parameterset values at the age estimate, (iii) iteratively calculating theinitial parameter set values, the weight estimate, the fasting bloodglucose concentration estimate, and the serum HbA1c estimate, in atime-dependent manner in which the age estimate is increased by a firsttime step in each iteration from the age estimate set in step (b)(i)until the age estimate is substantially equal to the age, and (iv)iteratively performing steps (ii)-(iii) until the weight estimate issubstantially equal to the weight, the fasting blood glucoseconcentration estimate is substantially equal to the fasting bloodglucose concentration, and the serum HbA1c estimate is substantiallyequal to the serum HbA1c concentration when the age estimate issubstantially equal to the age; c. iteratively calculating a future timeand a projected fasting blood glucose concentration using the initialparameter set calculated in step (b)(iii) and the subject parametervalues, in a time-dependent manner in which the first time is increasedby a second time step in each iteration until the projected fastingblood glucose concentration at the future time is calculated to begreater than 125 mg/dL; d. identifying the future time iterativelycalculated in step (c) as the time to diabetes onset; and e. displayingthe future time identified in step (d) as the predicted time fordiabetes onset.
 2. The method of claim 1, wherein the age estimate setin step (b)(i) is at least one year less than the age.
 3. The method ofclaim 1, wherein the weight estimate set in step (b)(i) is less than theweight.
 4. The method of claim 1, wherein the serum HbA1c estimate setin step (b)(i) is less than 3%.
 5. The method of claim 1, wherein thefasting blood glucose concentration estimate set in step (b)(i) is lessthan 100 mg/dL.
 6. The method of claim 1, wherein the first time step is5-365 days.
 7. The method of claim 6, wherein the first time step isconstant for all iterations.
 8. The method of claim 1, wherein thesecond time step is 5-365 days.
 9. The method of claim 8, wherein thesecond time step is constant for all iterations.
 10. The method of claim1, wherein receiving the subject parameter values comprises: receivingraw health data from one or more of patient specific electronic medicalrecords, published clinical studies, claims data, prescription data,patient biomarkers, and wearable user devices; receiving health dataabout the subject; and determining the subject parameter values of thesubject based on the received raw health data and the received subjecthealth data.
 11. The method of claim 1, wherein performing step (iv)comprises: adding a perturbation factor to one or more of the initialestimates for the initial parameter set values, wherein the perturbationfactor is configured to be adjustable in each increase to increase anaccuracy of calculating the initial parameter set values.
 12. The methodof claim 1, wherein performing step (c) of iteratively calculating thefuture time and the projected fasting blood glucose concentrationcomprises: receiving time-varying information comprising dietinformation, physical activity information, or both diet information andphysical activity information; and applying the time-varying informationduring each of the iteratively calculated future time and projectedfasting blood glucose concentration.
 13. The method of claim 12, whereinthe time-varying information comprises the diet information, and whereinthe diet information comprises consumption rates or amounts for aplurality of macronutrients.
 14. The method of claim 13, wherein theplurality of macronutrients comprises carbohydrates, fats, and proteins.15. A system, comprising a processor and a non-transitory memorycomputer readable medium storing programming instructions executable bythe processor, wherein the programming instructions comprise: a.determining, for the subject, subject parameter values comprisingheight, weight, age, gender, fasting blood glucose concentration, andserum HbA1c concentration at a first time; b. calculating initialparameter set values comprising basal metabolic rate, excess caloricintake, EC50(FFA), and beta cell apoptosis rate based on the subjectparameter values by (i) setting an age estimate to an age younger thanthe age determined in step (a), setting a weight estimate to a weightthat is different from the weight determined in step (a), setting afasting blood glucose concentration estimate to a value different fromthe fasting blood glucose concentration determined in step (a), andsetting a serum HbA1c estimate to a value different from the serum HbA1cconcentration determined in step (a), (ii) providing an initial estimatefor each value in the initial parameter set values at the age estimate,(iii) iteratively calculating the initial parameter set values, theweight estimate, the fasting blood glucose concentration estimate, andthe serum HbA1c estimate, in a time-dependent manner in which the ageestimate is increased by a first time step in each iteration from theage estimate set in step (b)(i) until the age estimate is substantiallyequal to the age, and (iv) iteratively performing steps (ii)-(iii) untilthe weight estimate is substantially equal to the weight, the fastingblood glucose concentration estimate is substantially equal to thefasting blood glucose concentration, and the serum HbA1c estimate issubstantially equal to the serum HbA1c concentration when the ageestimate is substantially equal to the age; c. iteratively calculating afuture time and a projected fasting blood glucose concentration usingthe initial parameter set calculated in step (b)(iii) and the subjectparameter values, in a time-dependent manner in which the first time isincreased by a second time step in each iteration until the projectedfasting blood glucose concentration at the future time is calculated tobe greater than 125 mg/dL; d. identifying the future time iterativelycalculated in step (c) as the time to diabetes onset; and e. displayingthe future time identified in step (d) as the predicted time fordiabetes onset.
 16. The system of claim 15, wherein the age estimate setin step (b)(i) is at least one year less than the age.
 17. The system ofclaim 15, wherein the weight estimate set in step (b)(i) is less thanthe weight.
 18. The system of claim 15, wherein the serum HbA1c estimateset in step (b)(i) is less than 3%.
 19. The system of claim 15, whereinthe fasting blood glucose concentration estimate set in step (b)(i) isless than 100 mg/dL.
 20. The system of claim 15, wherein the first timestep is 5-365 days.
 21. The system of claim 20, wherein the first timestep is constant for all iterations.
 22. The system of claim 15, whereinthe second time step is 5-365 days.
 23. The system of claim 22, whereinthe second time step is constant for all iterations.
 24. The system ofclaim 15, wherein receiving the subject parameter values comprises:receiving raw health data from one or more of patient specificelectronic medical records, published clinical studies, claims data,prescription data, patient biomarkers, and wearable user devices;receiving health data about the subject; and determining the subjectparameter values of the subject based on the received raw health dataand the received subject health data.
 25. The system of claim 15,wherein performing step (iv) comprises: adding a perturbation factor toone or more of the initial estimates for the initial parameter setvalues, wherein the perturbation factor is configured to be adjustablein each increase to increase an accuracy of calculating the initialparameter set values.
 26. The system of claim 15, wherein performingstep (c) of iteratively calculating the future time and the projectedfasting blood glucose concentration comprises: receiving time-varyinginformation comprising diet information, physical activity information,or both diet information and physical activity information; and applyingthe time-varying information during each of the iteratively calculatedfuture time and projected fasting blood glucose concentration.
 27. Thesystem of claim 26, wherein the time-varying information comprises thediet information, and wherein the diet information comprises consumptionrates or amounts for a plurality of macronutrients.
 28. The system ofclaim 27, wherein the plurality of macronutrients comprisescarbohydrates, fats, and proteins.